Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for optimizing production of a resource from an unconventional horizontal well, comprising: compiling values for predictor parameters and target parameters for each of a plurality of known wells; generating a model for anticipating production of said unconventional horizontal well, said model using empirical relationships between said predictor parameters and said target parameters to anticipate said production of said unconventional horizontal well; using a production optimizer and said model to determine a physical parameter change for increasing said anticipated production from said unconventional horizontal well; communicating said physical parameter change to a rig; and causing said rig to automatically make said physical parameter change to said unconventional horizontal well; wherein said predictor parameters include information about each of a well location, a geological feature, a well operator, an engineering feature, a well formation, a gas to oil ratio, and a well density, and wherein the plurality of known wells for which the values for predictor parameters are compiled includes at least one neighboring well, the at least one neighboring well being determined based upon data characterizing the neighboring well meeting a set of initial conditions.
This invention relates to optimizing resource production from unconventional horizontal wells, particularly by leveraging data from neighboring wells to improve output. The problem addressed is the inefficiency in traditional production methods, which often fail to account for the complex interactions between geological, operational, and engineering factors in unconventional reservoirs. The method involves compiling predictor parameters—such as well location, geological features, operator practices, engineering specifications, formation characteristics, gas-to-oil ratios, and well density—for multiple known wells, including at least one neighboring well that meets predefined initial conditions. These parameters are used to generate an empirical model that predicts production outcomes based on historical data. The model is then integrated with a production optimizer to identify physical adjustments (e.g., drilling parameters, completion strategies) that maximize anticipated production. The determined changes are automatically communicated to a drilling rig, which implements them without manual intervention. By incorporating data from neighboring wells and using predictive modeling, the system dynamically optimizes production in real-time, reducing guesswork and improving efficiency in unconventional reservoirs. The approach ensures that decisions are data-driven, accounting for local geological variations and operational constraints.
2. The method of claim 1 , wherein said target parameters include information about an initial decline rate and an initial production rate.
This invention relates to methods for analyzing and predicting production characteristics of a well, particularly in the oil and gas industry. The problem addressed is the need for accurate forecasting of well performance to optimize production and resource allocation. The method involves determining target parameters that define the well's production behavior over time, including an initial decline rate and an initial production rate. These parameters are used to model the well's expected output, allowing operators to make informed decisions about extraction strategies. The initial decline rate represents the rate at which production decreases from its peak, while the initial production rate indicates the well's output at the start of its productive life. By incorporating these factors, the method provides a more precise prediction of future production trends, helping to maximize efficiency and minimize waste. The technique may also integrate additional data, such as reservoir conditions or historical performance, to refine the model further. This approach ensures that production forecasts are based on realistic assumptions, reducing the risk of overestimation or underestimation of a well's potential. The method is particularly useful for managing mature fields where production decline is a critical factor in decision-making.
3. The method of claim 2 , wherein said model includes each of a qi model and a di model.
This invention relates to a system for analyzing and predicting the behavior of a physical system using a hybrid modeling approach. The system addresses the challenge of accurately modeling complex physical phenomena that involve both continuous and discrete dynamics. Traditional modeling techniques often struggle to capture the interplay between these dynamics, leading to inaccuracies in predictions. The system employs a hybrid model that integrates a continuous-time model (qi model) and a discrete-time model (di model). The qi model captures the continuous dynamics of the system, such as fluid flow, temperature changes, or mechanical vibrations, using differential equations or other continuous mathematical representations. The di model, on the other hand, handles discrete events or transitions, such as state changes, switching behaviors, or discrete control actions, using finite-state machines, event-driven logic, or other discrete mathematical frameworks. The hybrid model combines these two components to provide a comprehensive representation of the system's behavior. The continuous and discrete models interact through well-defined interfaces, allowing the system to transition smoothly between continuous and discrete states. This approach enables accurate predictions of system behavior over time, even when the system exhibits both continuous and discrete dynamics. The system may be applied in various domains, including robotics, automotive systems, industrial automation, and environmental monitoring, where accurate modeling of hybrid dynamics is critical. By leveraging both continuous and discrete models, the system improves prediction accuracy and robustness compared to traditional modeling techniques.
4. The method of claim 3 , wherein said physical parameter change includes at least one of: (a) varying an amount of proppant introduced in said unconventional horizontal well; (b) varying an amount of water introduced in said unconventional horizontal well; and (c) changing a well density.
This invention relates to optimizing hydraulic fracturing operations in unconventional horizontal wells, particularly by adjusting physical parameters to improve well performance. The method involves modifying at least one of three key parameters during fracturing: the amount of proppant introduced into the well, the amount of water used, or the well density. Proppant adjustments control the material used to keep fractures open, while water volume variations impact fracture propagation. Changing well density refers to altering the spacing or number of wells in a given area to optimize resource extraction. These modifications are applied to unconventional horizontal wells, which are drilled horizontally through low-permeability formations to access trapped hydrocarbons. The primary challenge addressed is enhancing production efficiency by tailoring fracturing operations to specific geological conditions. By systematically varying these parameters, the method aims to maximize hydrocarbon recovery while minimizing operational costs and environmental impact. The approach leverages data-driven adjustments to physical inputs, distinguishing it from traditional fixed-parameter fracturing techniques. This optimization strategy is particularly valuable in shale and tight oil/gas formations where conventional methods often yield suboptimal results.
5. The method of claim 3 , wherein said physical parameter change includes varying a lateral length of said unconventional horizontal well.
This invention relates to unconventional horizontal well drilling techniques, specifically addressing the challenge of optimizing well performance by adjusting physical parameters during drilling. The method involves modifying the lateral length of an unconventional horizontal well to enhance production efficiency. Unconventional wells, such as those in shale formations, often require precise lateral length adjustments to maximize hydrocarbon recovery while minimizing operational costs. The method includes drilling a horizontal well with a lateral section and dynamically varying its length based on real-time geological data, reservoir conditions, or production targets. This adjustment may involve extending or shortening the lateral section to improve fluid flow, reduce drilling risks, or adapt to subsurface variations. The technique may also incorporate additional steps like monitoring formation properties, adjusting drilling trajectories, or integrating data from adjacent wells to optimize the lateral length. By dynamically controlling the lateral length, the method aims to improve well productivity, reduce drilling time, and enhance overall economic viability. The approach is particularly useful in complex reservoirs where static well designs may not fully exploit available resources.
6. The method of claim 1 , further comprising determining a neighboring well based upon data characterizing the neighboring well meeting a set of relaxed conditions, said relaxed conditions being introduced based upon a determination that a set of initial conditions is unmet.
This invention relates to optimizing well placement in subsurface resource extraction, such as oil and gas drilling, where precise well targeting is critical for efficiency and yield. The problem addressed is the difficulty in identifying suitable neighboring wells when initial targeting criteria are too stringent, leading to suboptimal well placement or missed opportunities. The method involves analyzing data characterizing potential neighboring wells to determine their suitability. If the initial set of conditions for well placement is unmet—meaning no wells meet the strict criteria—the system introduces a set of relaxed conditions. These relaxed conditions are less stringent than the initial criteria, allowing for the identification of neighboring wells that would otherwise be excluded. By dynamically adjusting the selection criteria, the method ensures that viable well candidates are not overlooked due to overly restrictive parameters, improving operational flexibility and resource extraction efficiency. The invention enhances decision-making in well placement by balancing precision with adaptability, ensuring that wells are selected based on the most relevant and achievable criteria at any given time. This approach helps optimize drilling strategies, reduce costs, and maximize resource recovery.
7. The method of claim 1 , wherein said model is a machine learning model.
A machine learning model is used to analyze data and generate predictions or classifications. The model is trained on a dataset to learn patterns and relationships within the data. Once trained, the model can process new input data and produce outputs based on the learned patterns. This approach is applied to solve problems where traditional rule-based systems are ineffective or inefficient, such as in complex decision-making tasks, pattern recognition, or predictive analytics. The machine learning model may be a supervised, unsupervised, or reinforcement learning model, depending on the specific application. Supervised learning involves training the model on labeled data, where the correct outputs are known, while unsupervised learning identifies patterns in unlabeled data. Reinforcement learning involves training the model through interactions with an environment, receiving feedback in the form of rewards or penalties. The model may also be optimized using techniques such as hyperparameter tuning, cross-validation, or regularization to improve accuracy and generalization. This method is particularly useful in fields like healthcare, finance, and autonomous systems, where data-driven decision-making is critical. The model's performance is evaluated using metrics such as accuracy, precision, recall, or F1-score, depending on the problem domain.
8. The method of claim 1 , further comprising generating a warning signal and communicating said warning signal to an operator based upon a determination that said anticipated production of said unconventional horizontal well is suboptimal.
This invention relates to optimizing production in unconventional horizontal wells, particularly in scenarios where production performance deviates from expected levels. The method involves monitoring real-time data from the well, such as pressure, flow rates, and other operational parameters, to assess production efficiency. If the data indicates that the well's production is suboptimal—meaning it is not meeting predefined performance criteria or expected output levels—the system generates a warning signal. This signal is then communicated to an operator, alerting them to the issue so that corrective actions can be taken. The warning mechanism helps prevent prolonged suboptimal performance, which can lead to lost revenue or equipment damage. The system may also incorporate predictive analytics to anticipate future production declines based on current trends, allowing for proactive intervention. By integrating real-time monitoring with automated alerts, the method ensures timely responses to production inefficiencies, improving overall well performance and operational reliability. The invention is particularly useful in unconventional reservoirs where production variability is common, and early detection of issues is critical for maintaining optimal output.
9. A method for optimizing production of a resource from an unconventional horizontal well, comprising: compiling values for predictor parameters and target parameters for each of a plurality of known wells; generating a model for anticipating production of said unconventional horizontal well; using a production optimizer and said model to determine a physical parameter change for increasing said anticipated production from said unconventional horizontal well; communicating said physical parameter change to a rig; and causing said rig to make said physical parameter change to said unconventional horizontal well, wherein the plurality of known wells for which the values for predictor parameters are compiled includes at least one neighboring well, the at least one neighboring well being determined based upon data characterizing the neighboring well meeting a set of initial conditions.
The method optimizes production from unconventional horizontal wells by leveraging data from neighboring wells to improve output. Unconventional wells, such as those in shale formations, often exhibit complex production behaviors influenced by geological and operational factors. The method addresses the challenge of predicting and enhancing production by analyzing historical data from multiple wells, including nearby wells that meet specific initial conditions like geological similarity or operational proximity. The process begins by compiling predictor parameters (e.g., geological features, drilling conditions) and target parameters (e.g., production rates) for multiple known wells, including at least one neighboring well selected based on predefined criteria. A predictive model is then generated to forecast production for the target unconventional horizontal well. A production optimizer uses this model to identify physical parameter changes (e.g., drilling adjustments, completion techniques) that would increase anticipated production. These changes are communicated to a drilling rig, which implements them to enhance the well's output. The inclusion of neighboring well data improves model accuracy by accounting for local geological and operational influences. This approach enables real-time optimization of unconventional well production, reducing uncertainty and improving efficiency.
10. The method of claim 9 , further comprising periodically updating said values.
A system and method for dynamically adjusting operational parameters in a technical process involves monitoring performance metrics and modifying control variables to optimize efficiency. The method includes collecting real-time data from sensors or other monitoring devices, analyzing the data to determine deviations from desired performance thresholds, and automatically adjusting control parameters to correct those deviations. This ensures consistent output quality and operational stability. The system may also incorporate predictive algorithms to anticipate performance issues before they occur, allowing for preemptive adjustments. Additionally, the method includes periodically updating the stored values used for comparison and adjustment, ensuring the system remains calibrated to current operational conditions. This periodic updating may involve recalibrating sensor thresholds, refining predictive models, or adjusting control parameter ranges based on new data or changing environmental factors. The system is particularly useful in industrial automation, manufacturing, and process control applications where maintaining precise operational parameters is critical. By continuously monitoring and adjusting, the method improves system reliability, reduces downtime, and enhances overall efficiency.
11. The method of claim 9 , wherein said predictor parameters include information about each of a well location, a geological feature, a well operator, an engineering feature, a well formation, a gas to oil ratio, and a well density.
This invention relates to predictive modeling for oil and gas well performance, addressing the challenge of accurately forecasting well productivity and operational outcomes based on diverse geological and operational factors. The method involves generating predictor parameters that incorporate detailed information about well characteristics and environmental conditions to enhance prediction accuracy. These parameters include specific data points such as well location, geological features, well operator details, engineering specifications, well formation properties, gas-to-oil ratios, and well density. By integrating these variables, the system creates a comprehensive dataset that improves the reliability of predictive models for well performance. The method leverages this enriched data to train and refine predictive algorithms, enabling more precise forecasts of well productivity, operational efficiency, and potential risks. This approach helps operators optimize drilling strategies, resource allocation, and maintenance schedules, ultimately improving overall well performance and economic viability. The system's ability to process and analyze multiple interconnected factors ensures more accurate and actionable insights for decision-making in the oil and gas industry.
12. The method of claim 9 , wherein said target parameters include information about an initial decline rate and an initial production rate.
This invention relates to methods for analyzing and predicting production characteristics of a well, particularly in the oil and gas industry. The problem addressed is the need for accurate forecasting of well performance, which is critical for optimizing production and resource management. The method involves determining target parameters that describe the well's behavior over time, with a focus on initial decline rate and initial production rate. These parameters are used to model the well's production profile, allowing operators to predict future output and make informed decisions. The initial decline rate indicates how quickly production decreases after the well is brought online, while the initial production rate represents the well's output at the start of production. By incorporating these parameters, the method provides a more precise assessment of the well's performance, enabling better planning and resource allocation. The technique may be applied to various types of wells, including oil, gas, and water wells, to enhance production efficiency and economic viability. The method leverages historical data and mathematical models to refine predictions, ensuring reliable forecasting for long-term operations.
13. The method of claim 9 , wherein said model includes each of a qi model and a di model.
This invention relates to a method for analyzing data using a hybrid modeling approach to improve predictive accuracy. The method addresses the challenge of accurately modeling complex systems where traditional single-model approaches may fail to capture all relevant dynamics. The hybrid model combines a quasi-impulse (qi) model and a disturbance input (di) model to provide a more comprehensive representation of the system. The qi model captures rapid, transient changes in the system, while the di model accounts for slower, sustained disturbances. By integrating these two models, the method enhances the ability to predict system behavior under varying conditions. The hybrid model is trained using input data and system response data, allowing it to adapt to different scenarios. The method is particularly useful in applications where both transient and steady-state behaviors are critical, such as in control systems, signal processing, and predictive analytics. The combined model improves accuracy by reducing errors that arise from relying on a single modeling approach. The invention provides a flexible framework that can be applied to various domains where hybrid modeling is beneficial.
14. The method of claim 9 , wherein said physical parameter change includes varying an amount of proppant introduced in said unconventional horizontal well.
This invention relates to optimizing hydraulic fracturing operations in unconventional horizontal wells, particularly by dynamically adjusting physical parameters during the process to improve well productivity. The method involves monitoring and modifying operational variables in real-time to enhance fracture network development and proppant placement. A key aspect is varying the amount of proppant introduced into the wellbore during fracturing stages. Proppant, typically sand or ceramic materials, is used to keep fractures open after hydraulic pressure is released, allowing hydrocarbons to flow to the well. By adjusting proppant volume based on real-time data such as pressure, flow rate, or geological feedback, the method aims to prevent proppant bridging (blockages) while ensuring effective fracture conductivity. This dynamic control helps tailor fracturing treatments to specific geological conditions, improving efficiency and reducing costs. The approach integrates with other fracturing parameters like fluid viscosity and injection rate, which may also be adjusted to optimize fracture propagation and proppant distribution. The goal is to maximize hydrocarbon recovery while minimizing operational risks and material waste in unconventional reservoirs, where complex geologies often require precise, adaptive fracturing techniques.
15. The method of claim 9 , wherein said physical parameter change includes varying a lateral length of said unconventional horizontal well.
This invention relates to unconventional horizontal well drilling techniques, specifically addressing the challenge of optimizing well performance by adjusting physical parameters during drilling. The method involves modifying the lateral length of an unconventional horizontal well to enhance production efficiency. Unconventional wells, such as those in shale formations, often require precise lateral length adjustments to maximize hydrocarbon recovery while minimizing operational costs. The method includes drilling a horizontal well with a lateral section and dynamically varying its length based on real-time data, such as reservoir pressure, fluid flow rates, or geological feedback. This adjustment may involve extending or shortening the lateral section to optimize well productivity. The technique may also incorporate additional steps like monitoring well performance metrics, analyzing reservoir characteristics, and using predictive models to determine the optimal lateral length. By dynamically altering the lateral length, the method aims to improve well performance, reduce drilling risks, and enhance overall resource extraction efficiency. The approach is particularly useful in complex geological environments where static well designs may not yield optimal results.
16. The method of claim 9 , wherein said physical parameter change includes varying a fluid usable in a well drilling and producing operation.
This invention relates to well drilling and producing operations, specifically to methods for monitoring and adjusting physical parameters to optimize performance. The method involves detecting changes in a physical parameter during well operations, such as fluid properties, pressure, temperature, or flow rate, and dynamically adjusting the parameter to improve efficiency, safety, or productivity. The method includes using sensors to measure the parameter, analyzing the data to determine deviations from desired values, and automatically or manually adjusting the parameter to correct the deviation. In one embodiment, the physical parameter change involves varying the fluid used in the well, such as adjusting viscosity, density, or chemical composition to enhance drilling efficiency, prevent formation damage, or improve fluid recovery. The method may also involve integrating real-time data with predictive models to anticipate and mitigate potential issues before they impact operations. The system may include a control unit that processes sensor data, compares it to predefined thresholds, and triggers adjustments to fluid properties or other parameters to maintain optimal operating conditions. This approach ensures consistent performance, reduces downtime, and enhances overall well productivity.
17. A method for optimizing production of a resource from an unconventional horizontal well, comprising: compiling values for predictor parameters and target parameters for each of a plurality of known wells; generating a model for anticipating production of said unconventional horizontal well; using a production optimizer and said model to determine a physical parameter change for optimizing said anticipated production from said unconventional horizontal well; communicating said physical parameter change; and estimating a worth of said unconventional horizontal well over the course of its life based on said optimized production, wherein the plurality of known wells for which the values for predictor parameters are compiled includes at least one neighboring well, the at least one neighboring well being determined based upon data characterizing the neighboring well meeting a set of initial conditions.
This technical summary describes a method for optimizing resource production from unconventional horizontal wells, such as those used in shale oil or gas extraction. The method addresses the challenge of maximizing production efficiency and economic value by leveraging data from existing wells to predict and enhance future well performance. The method involves compiling values for predictor parameters (e.g., geological, operational, or completion characteristics) and target parameters (e.g., production rates) from multiple known wells, including at least one neighboring well that meets predefined initial conditions. These conditions may relate to proximity, geological similarity, or operational history. A predictive model is then generated to anticipate production outcomes for a new or existing unconventional horizontal well. A production optimizer uses this model to determine adjustments to physical parameters (e.g., drilling trajectory, fracturing pressure, or completion design) that would optimize anticipated production. The recommended changes are communicated to operators for implementation. Additionally, the method estimates the economic worth of the well over its lifecycle based on the optimized production forecast, providing a financial assessment of the optimization strategy. This approach improves decision-making by integrating historical well data, predictive modeling, and real-time optimization to enhance resource extraction efficiency and profitability.
18. The method of claim 17 , wherein each of said plurality of wells is a similarly situated neighboring well.
This invention relates to methods for managing or analyzing neighboring wells in a subsurface formation, such as in oil and gas extraction or geothermal energy applications. The problem addressed involves optimizing the performance, efficiency, or safety of multiple wells that are similarly situated in close proximity to one another. These wells may share similar geological conditions, operational parameters, or fluid flow characteristics, requiring coordinated management to avoid interference or to enhance collective output. The method involves monitoring and controlling a plurality of wells that are similarly situated, meaning they are positioned in a comparable manner relative to geological features, reservoir conditions, or production infrastructure. By treating these wells as a group with shared characteristics, the method enables more precise adjustments to extraction rates, injection pressures, or other operational variables. This approach helps prevent issues like well interference, reservoir depletion, or environmental impacts while maximizing resource recovery. The method may include steps such as collecting data from each well, analyzing the data to identify patterns or deviations, and applying corrective actions to maintain optimal performance across the group. Techniques like pressure management, flow rate adjustment, or chemical treatment may be used to ensure consistent and efficient operation. The method may also incorporate predictive modeling to anticipate and mitigate potential problems before they arise. By focusing on similarly situated neighboring wells, the method provides a systematic way to manage subsurface operations in a coordinated manner, improving overall efficiency and sustainability.
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December 8, 2020
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